MATEC Web Conf.
Volume 323, 202010th International Conference of Advanced Models and New Concepts in Concrete and Masonry Structures (AMCM 2020)
|Number of page(s)||8|
|Section||Adnanced Models and New Concepts in Masonry Structures|
|Published online||05 October 2020|
Evaluation of seismic performance measures for MDOF RC structures subjected to simulated and real ground motions
Civil Engineering Department, Middle East Technical University, Ankara, Turkey
* Corresponding author: email@example.com
Nonlinear time history analyses of structures require full time series of ground motion records. For regions with sparse seismic networks or potential large earthquakes, ground motion simulation has gained more attention in recent years. Simulated records are required to be generated using regional input dataset and then verified against existing recorded ground motions of past events. To use simulated ground motions in engineering applications, estimation of reliable seismic demand parameters is essential. In this study, the real and simulated records of the 2009 L’Aquila, Italy earthquake with (Mw=6.3) are investigated for their use in engineering practice. In the first step, misfits are evaluated for alternative seismological measures (peak values, duration and frequency as well as energy content of the time histories). Next, varying multi-degree-of-freedom reinforced concrete structures with different number of stories are selected. Numerical models of the structures are performed in the OpenSees platform. Seismic performance measures in terms of inter-story drift ratio for the selected structures are assessed through nonlinear time history analyses for both the real and simulated ground motions. Then, the misfits are estimated in terms of structural demand parameters. Results reveal a good fit between the seismological and engineering demand misfits for the selected ground motion simulation approaches.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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